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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.10696 |
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| _version_ | 1866913316752850944 |
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| author | Lu, Pengcheng Poesio, Massimo |
| author_facet | Lu, Pengcheng Poesio, Massimo |
| contents | Resolving coreference and bridging relations in chemical patents is important for better understanding the precise chemical process, where chemical domain knowledge is very critical. We proposed an approach incorporating external knowledge into a multi-task learning model for both coreference and bridging resolution in the chemical domain. The results show that integrating external knowledge can benefit both chemical coreference and bridging resolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10696 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain Lu, Pengcheng Poesio, Massimo Computation and Language Resolving coreference and bridging relations in chemical patents is important for better understanding the precise chemical process, where chemical domain knowledge is very critical. We proposed an approach incorporating external knowledge into a multi-task learning model for both coreference and bridging resolution in the chemical domain. The results show that integrating external knowledge can benefit both chemical coreference and bridging resolution. |
| title | Integrating knowledge bases to improve coreference and bridging resolution for the chemical domain |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.10696 |